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"""General IMPROVER metadata utilities"""
import hashlib
import pprint
import dask.array as da
import iris
import numpy as np
from improver.metadata.constants.attributes import (
MANDATORY_ATTRIBUTE_DEFAULTS, MANDATORY_ATTRIBUTES)
[docs]def create_new_diagnostic_cube(
name, units, coordinate_template, mandatory_attributes,
optional_attributes=None, data=None, dtype=np.float32):
"""
Creates a template for a new diagnostic cube with suitable metadata.
Args:
name (str):
Standard or long name for output cube
units (str or cf_units.Unit):
Units for output cube
coordinate_template (iris.cube.Cube):
Cube from which to copy dimensional and auxiliary coordinates
mandatory_attributes (dict):
Dictionary containing values for the mandatory attributes
"title", "source" and "institution". These are overridden by
values in the optional_attributes dictionary, if specified.
optional_attributes (dict or None):
Dictionary of optional attribute names and values. If values for
mandatory attributes are included in this dictionary they override
the values of mandatory_attributes.
data (numpy.ndarray or None):
Data array. If not set, cube is filled with zeros using a lazy
data object, as this will be overwritten later by the caller
routine.
dtype (numpy.dtype):
Datatype for dummy cube data if "data" argument is None.
Returns:
iris.cube.Cube:
Cube with correct metadata to accommodate new diagnostic field
"""
attributes = mandatory_attributes
if optional_attributes is not None:
attributes.update(optional_attributes)
error_msg = ""
for attr in MANDATORY_ATTRIBUTES:
if attr not in attributes:
error_msg += "{} attribute is required\n".format(attr)
if error_msg:
raise ValueError(error_msg)
if data is None:
data = da.zeros_like(coordinate_template.core_data(), dtype=dtype)
aux_coords_and_dims, dim_coords_and_dims = [
[(coord, coordinate_template.coord_dims(coord))
for coord in getattr(coordinate_template, coord_type)]
for coord_type in ('aux_coords', 'dim_coords')]
cube = iris.cube.Cube(
data, units=units, attributes=attributes,
dim_coords_and_dims=dim_coords_and_dims,
aux_coords_and_dims=aux_coords_and_dims)
cube.rename(name)
return cube
[docs]def generate_mandatory_attributes(diagnostic_cubes):
"""
Function to generate mandatory attributes for new diagnostics that are
generated using several different model diagnostics as input to the
calculation. If all input diagnostics have the same attribute use this,
otherwise set a default value.
Args:
diagnostic_cubes (list):
List of diagnostic cubes used in calculating the new diagnostic
Returns:
dict: Dictionary of mandatory attribute "key": "value" pairs.
"""
attributes = MANDATORY_ATTRIBUTE_DEFAULTS.copy()
for attr in MANDATORY_ATTRIBUTES:
try:
values = [cube.attributes[attr] for cube in diagnostic_cubes]
except KeyError:
# if not all input cubes have this attribute, retain default
pass
else:
unique_values = np.unique(values)
if len(unique_values) == 1:
attributes[attr] = unique_values[0]
return attributes
[docs]def generate_hash(data_in):
"""
Generate a hash from the data_in that can be used to uniquely identify
equivalent data_in.
Args:
data_in (any):
The data from which a hash is to be generated. This can be of any
type that can be pretty printed.
Returns:
str:
A hexadecimal string which is a hash hexdigest of the data as a
string.
"""
bytestring = pprint.pformat(data_in).encode('utf-8')
return hashlib.sha256(bytestring).hexdigest()
[docs]def create_coordinate_hash(cube):
"""
Generate a hash based on the input cube's x and y coordinates. This
acts as a unique identifier for the grid which can be used to allow two
grids to be compared.
Args:
cube (iris.cube.Cube):
The cube from which x and y coordinates will be used to
generate a hash.
Returns:
str:
A hash created using the x and y coordinates of the input cube.
"""
hashable_data = []
for axis in ('x', 'y'):
coord = cube.coord(axis=axis)
hashable_data.extend([
list(coord.points),
list(coord.bounds) if isinstance(coord.bounds, list) else None,
coord.standard_name,
coord.long_name,
coord.coord_system,
coord.units
])
return generate_hash(hashable_data)